A B C D E F G H I J K L M N O P Q R S T U V W X Z

E

e - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleState

The emission that is internally used for scoring subsequences.

Edge - Class in de.jstacs.algorithms.graphs

This class is a representation of a weighted edge.

Edge(int, int, double) - Constructor for class de.jstacs.algorithms.graphs.Edge

Creates a new weighted edge.

emission - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM

The emissions used in the states.

Emission - Interface in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions

This interface declares all method for an emission of a state.

emissionIdx - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM

The index of the used emission of each state.

emit(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous.HomogeneousMMDiffSM

This method returns a DataSet object containing artificial sequence(s).

emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel

emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM

emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM

emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM

emitDataSet(int, int...) - Method in interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel

This method returns a DataSet object containing artificial sequence(s).

emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel

emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM

Creates a DataSet of a given number of Sequences from a trained homogeneous model.

emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM

emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

emitDataSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.UniformTrainSM

emitDataSet(StatisticalModel, int) - Static method in class de.jstacs.utils.DiscreteInhomogenousDataSetEmitter

This method emits a sample with n sequences from the discrete inhomogeneous model m .

emitDataSetUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

The method returns an array of sequences using the current parameter set.

emitDataSetUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM

emitDataSetUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture

Standard implementation throwing an OperationNotSupportedException.

emitDataSetUsingCurrentParameterSet(int, int...) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.StrandTrainSM

emitSymbol(int[]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree

EmptyDataSetException - Exception in de.jstacs.data

An EmptyDataSetException will be thrown if no Sequence is in a DataSet (i.e. the DataSet is empty).

EmptyDataSetException() - Constructor for exception de.jstacs.data.EmptyDataSetException

This constructor creates an instance with default error message ("The created DataSet is empty.

encode(int[][]) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.FSDAGTrainSMParameterSet

This method can be used to encode an adjacency list to a graph description String (e.g. for the different constructors which requires graph description Strings).

enumerate(DifferentiableSequenceScore[], int, int, RecyclableSequenceEnumerator, double, DiffSSBasedOptimizableFunction, OutputStream) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox

This method allows to enumerate all possible seeds for a motif in the MutableMotifDiscoverer of a specific class.

enumerate(DifferentiableSequenceScore[], int[], int[], RecyclableSequenceEnumerator[], double, DiffSSBasedOptimizableFunction, OutputStream) - Static method in class de.jstacs.motifDiscovery.MutableMotifDiscovererToolbox

This method allows to enumerate all possible seeds for a number of motifs in the MutableMotifDiscoverers of a specific classes.

enumerateHP(Tensor) - Static method in class de.jstacs.algorithms.graphs.DAG

The method computes the HP(k) (see DAG).

EnumParameter - Class in de.jstacs.parameters

This class implements a SelectionParameter based on an Enum.

EnumParameter(Class<? extends Enum>, String, boolean) - Constructor for class de.jstacs.parameters.EnumParameter

The main constructor.

EnumParameter(Class<? extends Enum>, String, boolean, String) - Constructor for class de.jstacs.parameters.EnumParameter

This constructor creates an instance and set the default value.

EnumParameter(StringBuffer) - Constructor for class de.jstacs.parameters.EnumParameter

The standard constructor for the interface Storable.

eps - Variable in class de.jstacs.algorithms.optimization.NumericalDifferentiableFunction

The constant used in the computation of the gradient.

EqualParts - Class in de.jstacs.utils.random

This class is no real random generator it just returns 1/n for all values.

EqualParts() - Constructor for class de.jstacs.utils.random.EqualParts

equals(Object) - Method in class de.jstacs.data.sequences.Sequence

equals(Object) - Method in class de.jstacs.parameters.AbstractSelectionParameter

equals(Object) - Method in class de.jstacs.parameters.SequenceScoringParameterSet

equals(Object) - Method in class de.jstacs.parameters.SimpleParameter

EQUALS - Static variable in interface de.jstacs.parameters.validation.Constraint

The condition is equality

equals(Object) - Method in class de.jstacs.results.SimpleResult

equals(Object) - Method in class de.jstacs.utils.IntList

ErlangMRG - Class in de.jstacs.utils.random

This class is a multivariate random generator based on a Dirichlet distribution for alpha_i \in N.

ErlangMRG() - Constructor for class de.jstacs.utils.random.ErlangMRG

Constructor that creates a new multivariate random generator with underlying Erlang distribution.

ErlangMRGParams - Class in de.jstacs.utils.random

The container for parameters of an Erlang multivariate random generator.

ErlangMRGParams(int, int) - Constructor for class de.jstacs.utils.random.ErlangMRGParams

Constructor which creates a new hyperparameter vector for an Erlang random generator.

ErlangMRGParams(int[]) - Constructor for class de.jstacs.utils.random.ErlangMRGParams

Constructor which creates a new hyperparameter vector for an Erlang random generator.

errorMessage - Variable in class de.jstacs.parameters.AbstractSelectionParameter

If a value was illegal for the collection parameter, this field holds the error message.

errorMessage - Variable in class de.jstacs.parameters.ParameterSet

The error message of the last error or null

errorMessage - Variable in class de.jstacs.parameters.ParameterSetContainer

The message of the last error or null if no error occurred.

ess - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM

The equivalent sample size.

ess - Variable in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.DurationDiffSM

The equivalent sample size.

ess - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM

The equivalent sample size used for the prior

ess - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission

The equivalent sample sizes for each condition

ess - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement

The equivalent sample size (ess) used in the prior of this instance.

estimate(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint

Estimates the (smoothed) relative frequencies using the ess (equivalent sample size).

estimate(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb

estimate(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb

estimate(double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint

estimateComponentProbs - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

The switch for estimating the component probabilities or not.

estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous.GaussianEmission

estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission

estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission

estimateFromStatistic() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.Emission

This method estimates the parameters from the internal sufficient statistic.

estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.MixtureEmission

estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission

estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.UniformEmission

estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement

This method estimates the parameters from the sufficient statistic.

estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition

estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement

estimateFromStatistic() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement

estimateFromStatistic() - Method in interface de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.TrainableTransition

This method estimates the parameter of the transition using the internal sufficient statistic.

estimateFromStatistics() - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM

This method estimates the parameters of all emissions and the transition using their sufficient statistics.

estimateParameters(DataSet, double[]) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.DAGTrainSM

This method estimates the parameter of the model from the likelihood or the posterior, respectively.

estimateUnConditional(int, int, double, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint

Estimates unconditionally.

estimateUnConditional(int, int, double, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb

estimateUnConditional(double, double) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb

Estimates the unconditional frequencies using the ess (equivalent sample size).

estimateUnConditional(int, int, double, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb

eval(String) - Method in class de.jstacs.utils.REnvironment

Evaluates the String as R commands.

evaluate(PerformanceMeasureParameterSet, boolean, DataSet...) - Method in class de.jstacs.classifiers.AbstractClassifier

This method evaluates the classifier and computes, for instance, the sensitivity for a given specificity, the area under the ROC curve and so on.

evaluateClassifier(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, DataSet[], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.ClassifierAssessment

This method must be implemented in all subclasses.

evaluateClassifier(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, DataSet[], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.KFoldCrossValidation

Evaluates a classifier.

evaluateClassifier(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, DataSet[], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.RepeatedHoldOutExperiment

Evaluates the classifier.

evaluateClassifier(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, DataSet[], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.RepeatedSubSamplingExperiment

Evaluates the classifier.

evaluateClassifier(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, DataSet[], ProgressUpdater) - Method in class de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment

evaluateFunction(double[]) - Method in interface de.jstacs.algorithms.optimization.Function

Evaluates the function at a certain vector (in mathematical sense) x.

evaluateFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NegativeDifferentiableFunction

evaluateFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NegativeFunction

evaluateFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NegativeOneDimensionalFunction

evaluateFunction(double) - Method in class de.jstacs.algorithms.optimization.NegativeOneDimensionalFunction

evaluateFunction(double[]) - Method in class de.jstacs.algorithms.optimization.OneDimensionalFunction

evaluateFunction(double) - Method in class de.jstacs.algorithms.optimization.OneDimensionalFunction

Evaluates the function at position x.

evaluateFunction(double) - Method in class de.jstacs.algorithms.optimization.OneDimensionalSubFunction

evaluateFunction(double) - Method in class de.jstacs.algorithms.optimization.QuadraticFunction

evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction

evaluateFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction

This method evaluates the function for a part of the data.

evaluateFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LogGenDisMixFunction

evaluateFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.OneDataSetLogGenDisMixFunction

evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.CompositeLogPrior

evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.DoesNothingLogPrior

evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateGaussianLogPrior

evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SeparateLaplaceLogPrior

evaluateFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.SimpleGaussianSumLogPrior

evaluateGradientOfFunction(double[]) - Method in class de.jstacs.algorithms.optimization.DifferentiableFunction

Evaluates the gradient of a function at a certain vector (in mathematical sense) x, i.e., .

evaluateGradientOfFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NegativeDifferentiableFunction

evaluateGradientOfFunction(double[]) - Method in class de.jstacs.algorithms.optimization.NumericalDifferentiableFunction

Evaluates the gradient of a function at a certain vector (in mathematical sense) x numerically.

evaluateGradientOfFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction

evaluateGradientOfFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractMultiThreadedOptimizableFunction

This method evaluates the gradient of the function for a part of the data.

evaluateGradientOfFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LogGenDisMixFunction

evaluateGradientOfFunction(int, int, int, int, int) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.OneDataSetLogGenDisMixFunction

evaluateGradientOfFunction(double[]) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.LogPrior

EvaluationException - Exception in de.jstacs.algorithms.optimization

This class indicates that there was a problem to evaluate a function or the gradient of the function.

EvaluationException(String) - Constructor for exception de.jstacs.algorithms.optimization.EvaluationException

This constructor creates an EvaluationException with given error message.

ExpandableParameterSet - Class in de.jstacs.parameters

A class for a ParameterSet that can be expanded by additional Parameters at runtime.

ExpandableParameterSet(ParameterSet, String, String) - Constructor for class de.jstacs.parameters.ExpandableParameterSet

Creates a new ExpandableParameterSet from a Class that can be instantiated using this ExpandableParameterSet and templates for the ParameterSet in each element of the array, the name and the comment that are displayed for the ParameterSetContainers enclosing the ParameterSets.

ExpandableParameterSet(ParameterSet, String, String, int) - Constructor for class de.jstacs.parameters.ExpandableParameterSet

Creates a new ExpandableParameterSet from a Class that can be instantiated using this ExpandableParameterSet and templates for the ParameterSet in each element of the array, the name and the comment that are displayed for the ParameterSetContainers enclosing the ParameterSets.

ExpandableParameterSet(StringBuffer) - Constructor for class de.jstacs.parameters.ExpandableParameterSet

The standard constructor for the interface Storable.

ExpandableParameterSet(ParameterSet[], String, String) - Constructor for class de.jstacs.parameters.ExpandableParameterSet

Creates a new ExpandableParameterSet from a ParameterSet -array.

export(String, Result) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor

Exports a specified GalaxyAdaptor.LinkedImageResult of a program execution to a file provided by filename and returns the corresponding Galaxy data type.

ExtendedZOOPSDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

This class handles mixtures with at least one hidden motif.

ExtendedZOOPSDiffSM(boolean, int, int, boolean, HomogeneousDiffSM, DifferentiableStatisticalModel, DurationDiffSM, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM

This constructor creates an instance of ExtendedZOOPSDiffSM that is either an OOPS or a ZOOPS model depending on the chosen type.

ExtendedZOOPSDiffSM(boolean, int, int, boolean, HomogeneousDiffSM, DifferentiableStatisticalModel[], DurationDiffSM[], boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM

This constructor creates an instance of ExtendedZOOPSDiffSM that allows to have one site of the specified motifs in a Sequence.

ExtendedZOOPSDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM

This is the constructor for the interface Storable.

extendSampling(int, boolean) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier.DiffSMSamplingComponent

extendSampling(int, boolean) - Method in interface de.jstacs.sampling.SamplingComponent

This method allows to extend a sampling.

extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling

extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission

extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.SilentEmission

extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.SimpleSamplingState

extendSampling(int, boolean) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition

extendSampling(int) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

This method prepares the model to extend an existing sampling.

extract(int, String) - Static method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.ConstraintManager

Extracts the constraints of a String and returns an ArrayList of int[].

extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint

This method parses additional information from the StringBuffer that is not parsed in the base class.

extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM.HomCondProb

extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhCondProb

extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhConstraint

extractAdditionalInfo(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMConstraint

extractAdditionalInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.GaussianLikePositionPrior

extractAdditionalInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.PositionPrior

This method extracts additional information from a StringBuffer.

extractAdditionalInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.positionprior.UniformPositionPrior

extractForTag(StringBuffer, String) - Static method in class de.jstacs.io.XMLParser

Extracts the contents of source between tag start and end tags.

extractForTag(StringBuffer, String, Map<String, String>, Map<String, String>) - Static method in class de.jstacs.io.XMLParser

Extracts the contents of source between tag start and end tags.

extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.AbstractClassifier

Extracts further information of a classifier from an XML representation.

extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier

extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier

extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingGenDisMixClassifier

extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier

extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.differentiableSequenceScoreBased.ScoreClassifier

extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.MappingClassifier

extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier

extractFurtherClassifierInfosFromXML(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureClassifier

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS

This method is the opposite of IndependentProductDiffSS.getFurtherInformation().

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.differentiable.UniformDiffSS

This method is the opposite of UniformDiffSS.getFurtherInformation().

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.AbstractMixtureDiffSM

This method is the opposite of AbstractMixtureDiffSM.getFurtherInformation().

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif.ExtendedZOOPSDiffSM

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.StrandDiffSM

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.UniformDiffSM

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM

This method extracts further information from the XML representation.

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM

This method extracts further information from the XML representation.

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.AbstractConditionalDiscreteEmission

This method extracts further information from the XML representation.

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.PhyloDiscreteEmission

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete.ReferenceSequenceDiscreteEmission

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition.AbstractTransitionElement

This method extracts further information from the XML representation.

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition

This method extracts further information from the XML representation.

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicPluginTransitionElement

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicTransitionElement

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.DistanceBasedScaledTransitionElement

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ReferenceBasedTransitionElement

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.ScaledTransitionElement

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.HigherOrderTransition

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

This method is used in the subclasses to extract further information from the XML representation and to set these as values of the instance.

extractFurtherInformation(StringBuffer) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.AnnotatedEntity

This method can be used in the constructor with parameter StringBuffer to extract the further information.

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.classifiers.AbstractScoreBasedClassifier.DoubleTableResult

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.AbstractSelectionParameter

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.EnumParameter

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.FileParameter

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.MultiSelectionParameter

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.Parameter

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.ParameterSetContainer

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.RangeParameter

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.SelectionParameter

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.parameters.SimpleParameter

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.DataSetResult

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.ImageResult

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.ListResult

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.SimpleResult

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.results.StorableResult

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.FileResult

extractFurtherInfos(StringBuffer) - Method in class de.jstacs.utils.galaxy.GalaxyAdaptor.LinkedImageResult

extractObjectAndAttributesForTags(StringBuffer, String, Map<String, String>, Map<String, String>) - Static method in class de.jstacs.io.XMLParser

Returns the parsed value between the tags.

extractObjectAndAttributesForTags(StringBuffer, String, Map<String, String>, Map<String, String>, Class<T>) - Static method in class de.jstacs.io.XMLParser

Returns the parsed value between the tags.

extractObjectAndAttributesForTags(StringBuffer, String, Map<String, String>, Map<String, String>, Class<T>, Class<S>, S) - Static method in class de.jstacs.io.XMLParser

Returns the parsed value between the tags as an inner instance of the object outerInstance.

extractObjectForTags(StringBuffer, String) - Static method in class de.jstacs.io.XMLParser

Returns the parsed value between the tags.

extractObjectForTags(StringBuffer, String, Class<T>) - Static method in class de.jstacs.io.XMLParser

Returns the parsed value between the tags.

extractSequenceParts(int, DataSet[], DataSet[]) - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS

This method extracts the corresponding Sequence parts for a specific DifferentiableSequenceScore.

extractWeights(int, double[][]) - Method in class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS

This method creates the weights for IndependentProductDiffSS.extractSequenceParts(int, DataSet[], DataSet[]).